Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model

TL;DR

This paper introduces an adaptive, evolving neuro-neo-fuzzy-ANARX model designed for real-time forecasting of non-stationary, nonlinear time series, capable of online data stream processing.

Contribution

It presents a novel evolving weighted neuro-neo-fuzzy-ANARX model with learning procedures for online, adaptive time series forecasting.

Findings

Effective online processing of non-stationary data streams

Improved forecasting accuracy for nonlinear time series

Flexible model structure adapts to data changes

Abstract

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.